API

Modules
​concrete.ml.common: Module for shared data structures and code.
​concrete.ml.common.check_inputs: Check and conversion tools.
​concrete.ml.common.debugging: Module for debugging.
​concrete.ml.common.debugging.custom_assert: Provide some variants of assert.
​concrete.ml.common.serialization​
​concrete.ml.common.serialization.dumpers: Dump functions for serialization.
​concrete.ml.common.serialization.encoder: Custom encoder for serialization.
​concrete.ml.common.serialization.loaders: Load functions for serialization.
​concrete.ml.common.utils: Utils that can be re-used by other pieces of code in the module.
​concrete.ml.deployment: Module for deployment of the FHE model.
​concrete.ml.deployment.deploy_to_aws: Methods to deploy a client/server to AWS.
​concrete.ml.deployment.deploy_to_docker: Methods to deploy a server using Docker.
​concrete.ml.deployment.fhe_client_server: APIs for FHE deployment.
​concrete.ml.deployment.server: Deployment server.
​concrete.ml.deployment.utils: Utils.
​concrete.ml.onnx: ONNX module.
​concrete.ml.onnx.convert: ONNX conversion related code.
​concrete.ml.onnx.onnx_impl_utils: Utility functions for onnx operator implementations.
​concrete.ml.onnx.onnx_model_manipulations: Some code to manipulate models.
​concrete.ml.onnx.onnx_utils: Utils to interpret an ONNX model with numpy.
​concrete.ml.onnx.ops_impl: ONNX ops implementation in Python + NumPy.
​concrete.ml.pytest: Module which is used to contain common functions for pytest.
​concrete.ml.pytest.torch_models: Torch modules for our pytests.
​concrete.ml.pytest.utils: Common functions or lists for test files, which can't be put in fixtures.
​concrete.ml.quantization: Modules for quantization.
​concrete.ml.quantization.base_quantized_op: Base Quantized Op class that implements quantization for a float numpy op.
​concrete.ml.quantization.post_training: Post Training Quantization methods.
​concrete.ml.quantization.quantized_module: QuantizedModule API.
​concrete.ml.quantization.quantized_ops: Quantized versions of the ONNX operators for post training quantization.
​concrete.ml.quantization.quantizers: Quantization utilities for a numpy array/tensor.
​concrete.ml.search_parameters: Modules for p_error search.
​concrete.ml.search_parameters.p_error_search: p_error binary search for classification and regression tasks.
​concrete.ml.sklearn: Import sklearn models.
​concrete.ml.sklearn.base: Base classes for all estimators.
​concrete.ml.sklearn.glm: Implement sklearn's Generalized Linear Models (GLM).
​concrete.ml.sklearn.linear_model: Implement sklearn linear model.
​concrete.ml.sklearn.qnn: Scikit-learn interface for fully-connected quantized neural networks.
​concrete.ml.sklearn.qnn_module: Sparse Quantized Neural Network torch module.
​concrete.ml.sklearn.rf: Implement RandomForest models.
​concrete.ml.sklearn.svm: Implement Support Vector Machine.
​concrete.ml.sklearn.tree: Implement DecisionTree models.
​concrete.ml.sklearn.tree_to_numpy: Implements the conversion of a tree model to a numpy function.
​concrete.ml.sklearn.xgb: Implements XGBoost models.
​concrete.ml.torch: Modules for torch to numpy conversion.
​concrete.ml.torch.compile: torch compilation function.
​concrete.ml.torch.numpy_module: A torch to numpy module.
​concrete.ml.version: File to manage the version of the package.
Classes
​encoder.CustomEncoder: CustomEncoder: custom json encoder to handle non-native types.
​utils.FheMode: Enum representing the execution mode.
​deploy_to_aws.AWSInstance: AWSInstance.
​fhe_client_server.FHEModelClient: Client API to encrypt and decrypt FHE data.
​fhe_client_server.FHEModelDev: Dev API to save the model and then load and run the FHE circuit.
​fhe_client_server.FHEModelServer: Server API to load and run the FHE circuit.
​ops_impl.ONNXMixedFunction: A mixed quantized-raw valued onnx function.
​ops_impl.RawOpOutput: Type construct that marks an ndarray as a raw output of a quantized op.
​torch_models.BranchingGemmModule: Torch model with some branching and skip connections.
​torch_models.BranchingModule: Torch model with some branching and skip connections.
​torch_models.CNN: Torch CNN model for the tests.
​torch_models.CNNGrouped: Torch CNN model with grouped convolution for compile torch tests.
​torch_models.CNNInvalid: Torch CNN model for the tests.
​torch_models.CNNMaxPool: Torch CNN model for the tests with a max pool.
​torch_models.CNNOther: Torch CNN model for the tests.
​torch_models.DoubleQuantQATMixNet: Torch model that with two different quantizers on the input.
​torch_models.FC: Torch model for the tests.
​torch_models.FCSeq: Torch model that should generate MatMul->Add ONNX patterns.
​torch_models.FCSeqAddBiasVec: Torch model that should generate MatMul->Add ONNX patterns.
​torch_models.FCSmall: Torch model for the tests.
​torch_models.MultiInputNN: Torch model to test multiple inputs forward.
​torch_models.MultiInputNNConfigurable: Torch model to test multiple inputs forward.
​torch_models.MultiOpOnSingleInputConvNN: Network that applies two quantized operations on a single input.
​torch_models.NetWithConcatUnsqueeze: Torch model to test the concat and unsqueeze operators.
​torch_models.NetWithConstantsFoldedBeforeOps: Torch QAT model that does not quantize the inputs.
​torch_models.NetWithLoops: Torch model, where we reuse some elements in a loop.
​torch_models.PaddingNet: Torch QAT model that applies various padding patterns.
​torch_models.QATTestModule: Torch model that implements a simple non-uniform quantizer.
​torch_models.QNNFashionMNIST: A small quantized network with Brevitas for FashionMNIST classification.
​torch_models.QuantCustomModel: A small quantized network with Brevitas, trained on make_classification.
​torch_models.ShapeOperationsNet: Torch QAT model that reshapes the input.
​torch_models.SimpleNet: Fake torch model used to generate some onnx.
​torch_models.SimpleQAT: Torch model implements a step function that needs Greater, Cast and Where.
​torch_models.SingleMixNet: Torch model that with a single conv layer that produces the output, eg a blur filter.
​torch_models.StepActivationModule: Torch model implements a step function that needs Greater, Cast and Where.
​torch_models.TinyCNN: A very small CNN.
​torch_models.TinyQATCNN: A very small QAT CNN to classify the sklearn digits data-set.
​torch_models.TorchCustomModel: A small network with Brevitas, trained on make_classification.
​torch_models.TorchSum: Torch model to test the ReduceSum ONNX operator in a leveled circuit.
​torch_models.TorchSumMod: Torch model to test the ReduceSum ONNX operator in a circuit containing a PBS.
​torch_models.UnivariateModule: Torch model that calls univariate and shape functions of torch.
​base_quantized_op.QuantizedMixingOp: An operator that mixes (adds or multiplies) together encrypted inputs.
​base_quantized_op.QuantizedOp: Base class for quantized ONNX ops implemented in numpy.
​base_quantized_op.QuantizedOpUnivariateOfEncrypted: An univariate operator of an encrypted value.
​post_training.ONNXConverter: Base ONNX to Concrete ML computation graph conversion class.
​post_training.PostTrainingAffineQuantization: Post-training Affine Quantization.
​post_training.PostTrainingQATImporter: Converter of Quantization Aware Training networks.
​quantized_module.QuantizedModule: Inference for a quantized model.
​quantized_ops.ONNXConstantOfShape: ConstantOfShape operator.
​quantized_ops.ONNXGather: Gather operator.
​quantized_ops.ONNXShape: Shape operator.
​quantized_ops.ONNXSlice: Slice operator.
​quantized_ops.QuantizedAbs: Quantized Abs op.
​quantized_ops.QuantizedAdd: Quantized Addition operator.
​quantized_ops.QuantizedAvgPool: Quantized Average Pooling op.
​quantized_ops.QuantizedBatchNormalization: Quantized Batch normalization with encrypted input and in-the-clear normalization params.
​quantized_ops.QuantizedBrevitasQuant: Brevitas uniform quantization with encrypted input.
​quantized_ops.QuantizedCast: Cast the input to the required data type.
​quantized_ops.QuantizedCelu: Quantized Celu op.
​quantized_ops.QuantizedClip: Quantized clip op.
​quantized_ops.QuantizedConcat: Concatenate operator.
​quantized_ops.QuantizedConv: Quantized Conv op.
​quantized_ops.QuantizedDiv: Div operator /.
​quantized_ops.QuantizedElu: Quantized Elu op.
​quantized_ops.QuantizedErf: Quantized erf op.
​quantized_ops.QuantizedExp: Quantized Exp op.
​quantized_ops.QuantizedFlatten: Quantized flatten for encrypted inputs.
​quantized_ops.QuantizedFloor: Quantized Floor op.
​quantized_ops.QuantizedGemm: Quantized Gemm op.
​quantized_ops.QuantizedGreater: Comparison operator >.
​quantized_ops.QuantizedGreaterOrEqual: Comparison operator >=.
​quantized_ops.QuantizedHardSigmoid: Quantized HardSigmoid op.
​quantized_ops.QuantizedHardSwish: Quantized Hardswish op.
​quantized_ops.QuantizedIdentity: Quantized Identity op.
​quantized_ops.QuantizedLeakyRelu: Quantized LeakyRelu op.
​quantized_ops.QuantizedLess: Comparison operator <.
​quantized_ops.QuantizedLessOrEqual: Comparison operator <=.
​quantized_ops.QuantizedLog: Quantized Log op.
​quantized_ops.QuantizedMatMul: Quantized MatMul op.
​quantized_ops.QuantizedMax: Quantized Max op.
​quantized_ops.QuantizedMaxPool: Quantized Max Pooling op.
​quantized_ops.QuantizedMin: Quantized Min op.
​quantized_ops.QuantizedMul: Multiplication operator.
​quantized_ops.QuantizedNeg: Quantized Neg op.
​quantized_ops.QuantizedNot: Quantized Not op.
​quantized_ops.QuantizedOr: Or operator ||.
​quantized_ops.QuantizedPRelu: Quantized PRelu op.
​quantized_ops.QuantizedPad: Quantized Padding op.
​quantized_ops.QuantizedPow: Quantized pow op.
​quantized_ops.QuantizedReduceSum: ReduceSum with encrypted input.
​quantized_ops.QuantizedRelu: Quantized Relu op.
​quantized_ops.QuantizedReshape: Quantized Reshape op.
​quantized_ops.QuantizedRound: Quantized round op.
​quantized_ops.QuantizedSelu: Quantized Selu op.
​quantized_ops.QuantizedSigmoid: Quantized sigmoid op.
​quantized_ops.QuantizedSign: Quantized Neg op.
​quantized_ops.QuantizedSoftplus: Quantized Softplus op.
​quantized_ops.QuantizedSqueeze: Squeeze operator.
​quantized_ops.QuantizedSub: Subtraction operator.
​quantized_ops.QuantizedTanh: Quantized Tanh op.
​quantized_ops.QuantizedTranspose: Transpose operator for quantized inputs.
​quantized_ops.QuantizedUnsqueeze: Unsqueeze operator.
​quantized_ops.QuantizedWhere: Where operator on quantized arrays.
​quantizers.MinMaxQuantizationStats: Calibration set statistics.
​quantizers.QuantizationOptions: Options for quantization.
​quantizers.QuantizedArray: Abstraction of quantized array.
​quantizers.UniformQuantizationParameters: Quantization parameters for uniform quantization.
​quantizers.UniformQuantizer: Uniform quantizer.
​p_error_search.BinarySearch: Class for p_error hyper-parameter search for classification and regression tasks.
​base.BaseClassifier: Base class for linear and tree-based classifiers in Concrete ML.
​base.BaseEstimator: Base class for all estimators in Concrete ML.
​base.BaseTreeClassifierMixin: Mixin class for tree-based classifiers.
​base.BaseTreeEstimatorMixin: Mixin class for tree-based estimators.
​base.BaseTreeRegressorMixin: Mixin class for tree-based regressors.
​base.QuantizedTorchEstimatorMixin: Mixin that provides quantization for a torch module and follows the Estimator API.
​base.SklearnLinearClassifierMixin: A Mixin class for sklearn linear classifiers with FHE.
​base.SklearnLinearModelMixin: A Mixin class for sklearn linear models with FHE.
​base.SklearnLinearRegressorMixin: A Mixin class for sklearn linear regressors with FHE.
​glm.GammaRegressor: A Gamma regression model with FHE.
​glm.PoissonRegressor: A Poisson regression model with FHE.
​glm.TweedieRegressor: A Tweedie regression model with FHE.
​linear_model.ElasticNet: An ElasticNet regression model with FHE.
​linear_model.Lasso: A Lasso regression model with FHE.
​linear_model.LinearRegression: A linear regression model with FHE.
​linear_model.LogisticRegression: A logistic regression model with FHE.
​linear_model.Ridge: A Ridge regression model with FHE.
​qnn.NeuralNetClassifier: A Fully-Connected Neural Network classifier with FHE.
​qnn.NeuralNetRegressor: A Fully-Connected Neural Network regressor with FHE.
​qnn_module.SparseQuantNeuralNetwork: Sparse Quantized Neural Network.
​rf.RandomForestClassifier: Implements the RandomForest classifier.
​rf.RandomForestRegressor: Implements the RandomForest regressor.
​svm.LinearSVC: A Classification Support Vector Machine (SVM).
​svm.LinearSVR: A Regression Support Vector Machine (SVM).
​tree.DecisionTreeClassifier: Implements the sklearn DecisionTreeClassifier.
​tree.DecisionTreeRegressor: Implements the sklearn DecisionTreeClassifier.
​xgb.XGBClassifier: Implements the XGBoost classifier.
​xgb.XGBRegressor: Implements the XGBoost regressor.
​numpy_module.NumpyModule: General interface to transform a torch.nn.Module to numpy module.
Functions
​check_inputs.check_X_y_and_assert: sklearn.utils.check_X_y with an assert.
​check_inputs.check_X_y_and_assert_multi_output: sklearn.utils.check_X_y with an assert and multi-output handling.
​check_inputs.check_array_and_assert: sklearn.utils.check_array with an assert.
​custom_assert.assert_false: Provide a custom assert to check that the condition is False.
​custom_assert.assert_not_reached: Provide a custom assert to check that a piece of code is never reached.
​custom_assert.assert_true: Provide a custom assert to check that the condition is True.
​dumpers.dump: Dump any Concrete ML object that has a dump method.
​dumpers.dumps: Dump as string any object.
​dumpers.dumps_random_state: Dump random state to string.
​encoder.dumps_onnx: Dump onnx model as string.
​loaders.load: Load any Concrete ML object that has a dump method.
​loaders.load_dict: Load any Concrete ML object that has a dump method.
​loaders.loads: Load any Concrete ML object that has a dump method.
​loaders.loads_onnx: Load serialized onnx model.
​loaders.loads_random_state: Load random state from string.
​utils.all_values_are_floats: Indicate that all unpacked values are of a supported float dtype.
​utils.all_values_are_integers: Indicate that all unpacked values are of a supported integer dtype.
​utils.check_dtype_and_cast: Convert any allowed type into an array and cast it if required.
​utils.check_there_is_no_p_error_options_in_configuration: Check the user did not set p_error or global_p_error in configuration.
​utils.compute_bits_precision: Compute the number of bits required to represent x.
​utils.generate_proxy_function: Generate a proxy function for a function accepting only *args type arguments.
​utils.get_model_class: Return the class of the model (instantiated or not), which can be a partial() instance.
​utils.get_model_name: Return the name of the model, which can be a partial() instance.
​utils.get_onnx_opset_version: Return the ONNX opset_version.
​utils.is_brevitas_model: Check if a model is a Brevitas type.
​utils.is_classifier_or_partial_classifier: Indicate if the model class represents a classifier.
​utils.is_model_class_in_a_list: Indicate if a model class, which can be a partial() instance, is an element of a_list.
​utils.is_pandas_dataframe: Indicate if the input container is a Pandas DataFrame.
​utils.is_pandas_series: Indicate if the input container is a Pandas Series.
​utils.is_pandas_type: Indicate if the input container is a Pandas DataFrame or Series.
​utils.is_regressor_or_partial_regressor: Indicate if the model class represents a regressor.
​utils.manage_parameters_for_pbs_errors: Return (p_error, global_p_error) that we want to give to Concrete.
​utils.replace_invalid_arg_name_chars: Sanitize arg_name, replacing invalid chars by _.
​utils.to_tuple: Make the input a tuple if it is not already the case.
​deploy_to_aws.create_instance: Create a EC2 instance.
​deploy_to_aws.delete_security_group: Terminate a AWS EC2 instance.
​deploy_to_aws.deploy_to_aws: Deploy a model to a EC2 AWS instance.
​deploy_to_aws.main: Deploy a model.
​deploy_to_aws.terminate_instance: Terminate a AWS EC2 instance.
​deploy_to_aws.wait_instance_termination: Wait for AWS EC2 instance termination.
​deploy_to_docker.build_docker_image: Build server Docker image.
​deploy_to_docker.delete_image: Delete a Docker image.
​deploy_to_docker.main: Deploy function.
​deploy_to_docker.stop_container: Kill all containers that use a given image.
​utils.filter_logs: Filter logs based on previous logs.
​utils.is_connection_available: Check if ssh connection is available.
​utils.wait_for_connection_to_be_available: Wait for connection to be available.
​convert.get_equivalent_numpy_forward: Get the numpy equivalent forward of the provided ONNX model.
​convert.get_equivalent_numpy_forward_and_onnx_model: Get the numpy equivalent forward of the provided torch Module.
​onnx_impl_utils.compute_conv_output_dims: Compute the output shape of a pool or conv operation.
​onnx_impl_utils.compute_onnx_pool_padding: Compute any additional padding needed to compute pooling layers.
​onnx_impl_utils.numpy_onnx_pad: Pad a tensor according to ONNX spec, using an optional custom pad value.
​onnx_impl_utils.onnx_avgpool_compute_norm_const: Compute the average pooling normalization constant.
​onnx_model_manipulations.clean_graph_after_node_op_type: Clean the graph of the onnx model by removing nodes after the given node type.
​onnx_model_manipulations.clean_graph_at_node_op_type: Clean the graph of the onnx model by removing nodes at the given node type.
​onnx_model_manipulations.keep_following_outputs_discard_others: Keep the outputs given in outputs_to_keep and remove the others from the model.
​onnx_model_manipulations.remove_identity_nodes: Remove identity nodes from a model.
​onnx_model_manipulations.remove_node_types: Remove unnecessary nodes from the ONNX graph.
​onnx_model_manipulations.remove_unused_constant_nodes: Remove unused Constant nodes in the provided onnx model.
​onnx_model_manipulations.simplify_onnx_model: Simplify an ONNX model, removes unused Constant nodes and Identity nodes.
​onnx_utils.execute_onnx_with_numpy: Execute the provided ONNX graph on the given inputs.
​onnx_utils.get_attribute: Get the attribute from an ONNX AttributeProto.
​onnx_utils.get_op_type: Construct the qualified type name of the ONNX operator.
​onnx_utils.remove_initializer_from_input: Remove initializers from model inputs.
​ops_impl.cast_to_float: Cast values to floating points.
​ops_impl.numpy_abs: Compute abs in numpy according to ONNX spec.
​ops_impl.numpy_acos: Compute acos in numpy according to ONNX spec.
​ops_impl.numpy_acosh: Compute acosh in numpy according to ONNX spec.
​ops_impl.numpy_add: Compute add in numpy according to ONNX spec.
​ops_impl.numpy_asin: Compute asin in numpy according to ONNX spec.
​ops_impl.numpy_asinh: Compute sinh in numpy according to ONNX spec.
​ops_impl.numpy_atan: Compute atan in numpy according to ONNX spec.
​ops_impl.numpy_atanh: Compute atanh in numpy according to ONNX spec.
​ops_impl.numpy_avgpool: Compute Average Pooling using Torch.
​ops_impl.numpy_batchnorm: Compute the batch normalization of the input tensor.
​ops_impl.numpy_cast: Execute ONNX cast in Numpy.
​ops_impl.numpy_celu: Compute celu in numpy according to ONNX spec.
​ops_impl.numpy_concatenate: Apply concatenate in numpy according to ONNX spec.
​ops_impl.numpy_constant: Return the constant passed as a kwarg.
​ops_impl.numpy_cos: Compute cos in numpy according to ONNX spec.
​ops_impl.numpy_cosh: Compute cosh in numpy according to ONNX spec.
​ops_impl.numpy_div: Compute div in numpy according to ONNX spec.
​ops_impl.numpy_elu: Compute elu in numpy according to ONNX spec.
​ops_impl.numpy_equal: Compute equal in numpy according to ONNX spec.
​ops_impl.numpy_erf: Compute erf in numpy according to ONNX spec.
​ops_impl.numpy_exp: Compute exponential in numpy according to ONNX spec.
​ops_impl.numpy_flatten: Flatten a tensor into a 2d array.
​ops_impl.numpy_floor: Compute Floor in numpy according to ONNX spec.
​ops_impl.numpy_greater: Compute greater in numpy according to ONNX spec.
​ops_impl.numpy_greater_float: Compute greater in numpy according to ONNX spec and cast outputs to floats.
​ops_impl.numpy_greater_or_equal: Compute greater or equal in numpy according to ONNX spec.
​ops_impl.numpy_greater_or_equal_float: Compute greater or equal in numpy according to ONNX specs and cast outputs to floats.
​ops_impl.numpy_hardsigmoid: Compute hardsigmoid in numpy according to ONNX spec.
​ops_impl.numpy_hardswish: Compute hardswish in numpy according to ONNX spec.
​ops_impl.numpy_identity: Compute identity in numpy according to ONNX spec.
​ops_impl.numpy_leakyrelu: Compute leakyrelu in numpy according to ONNX spec.
​ops_impl.numpy_less: Compute less in numpy according to ONNX spec.
​ops_impl.numpy_less_float: Compute less in numpy according to ONNX spec and cast outputs to floats.
​ops_impl.numpy_less_or_equal: Compute less or equal in numpy according to ONNX spec.
​ops_impl.numpy_less_or_equal_float: Compute less or equal in numpy according to ONNX spec and cast outputs to floats.
​ops_impl.numpy_log: Compute log in numpy according to ONNX spec.
​ops_impl.numpy_matmul: Compute matmul in numpy according to ONNX spec.
​ops_impl.numpy_max: Compute Max in numpy according to ONNX spec.
​ops_impl.numpy_maxpool: Compute Max Pooling using Torch.
​ops_impl.numpy_min: Compute Min in numpy according to ONNX spec.
​ops_impl.numpy_mul: Compute mul in numpy according to ONNX spec.
​ops_impl.numpy_neg: Compute Negative in numpy according to ONNX spec.
​ops_impl.numpy_not: Compute not in numpy according to ONNX spec.
​ops_impl.numpy_not_float: Compute not in numpy according to ONNX spec and cast outputs to floats.
​ops_impl.numpy_or: Compute or in numpy according to ONNX spec.
​ops_impl.numpy_or_float: Compute or in numpy according to ONNX spec and cast outputs to floats.
​ops_impl.numpy_pow: Compute pow in numpy according to ONNX spec.
​ops_impl.numpy_relu: Compute relu in numpy according to ONNX spec.
​ops_impl.numpy_round: Compute round in numpy according to ONNX spec.
​ops_impl.numpy_selu: Compute selu in numpy according to ONNX spec.
​ops_impl.numpy_sigmoid: Compute sigmoid in numpy according to ONNX spec.
​ops_impl.numpy_sign: Compute Sign in numpy according to ONNX spec.
​ops_impl.numpy_sin: Compute sin in numpy according to ONNX spec.
​ops_impl.numpy_sinh: Compute sinh in numpy according to ONNX spec.
​ops_impl.numpy_softmax: Compute softmax in numpy according to ONNX spec.
​ops_impl.numpy_softplus: Compute softplus in numpy according to ONNX spec.
​ops_impl.numpy_sub: Compute sub in numpy according to ONNX spec.
​ops_impl.numpy_tan: Compute tan in numpy according to ONNX spec.
​ops_impl.numpy_tanh: Compute tanh in numpy according to ONNX spec.
​ops_impl.numpy_thresholdedrelu: Compute thresholdedrelu in numpy according to ONNX spec.
​ops_impl.numpy_transpose: Transpose in numpy according to ONNX spec.
​ops_impl.numpy_where: Compute the equivalent of numpy.where.
​ops_impl.numpy_where_body: Compute the equivalent of numpy.where.
​ops_impl.onnx_func_raw_args: Decorate a numpy onnx function to flag the raw/non quantized inputs.
​utils.data_calibration_processing: Reduce size of the given data-set.
​utils.get_random_extract_of_sklearn_models_and_datasets: Return a random sublist of sklearn_models_and_datasets.
​utils.get_torchvision_dataset: Get train or testing data-set.
​utils.instantiate_model_generic: Instantiate any Concrete ML model type.
​utils.load_torch_model: Load an object saved with torch.save() from a file or dict.
​post_training.get_n_bits_dict: Convert the n_bits parameter into a proper dictionary.
​quantizers.fill_from_kwargs: Fill a parameter set structure from kwargs parameters.
​p_error_search.compile_and_simulated_fhe_inference: Get the quantized module of a given model in FHE, simulated or not.
​sklearn.get_sklearn_linear_models: Return the list of available linear models in Concrete ML.
​sklearn.get_sklearn_models: Return the list of available models in Concrete ML.
​sklearn.get_sklearn_neural_net_models: Return the list of available neural net models in Concrete ML.
​sklearn.get_sklearn_tree_models: Return the list of available tree models in Concrete ML.
​tree_to_numpy.add_transpose_after_last_node: Add transpose after last node.
​tree_to_numpy.get_onnx_model: Create ONNX model with Hummingbird convert method.
​tree_to_numpy.preprocess_tree_predictions: Apply post-processing from the graph.
​tree_to_numpy.tree_onnx_graph_preprocessing: Apply pre-processing onto the ONNX graph.
​tree_to_numpy.tree_to_numpy: Convert the tree inference to a numpy functions using Hummingbird.
​tree_to_numpy.tree_values_preprocessing: Pre-process tree values.
​tree_to_numpy.workaround_squeeze_node_xgboost: Workaround to fix torch issue that does not export the proper axis in the ONNX squeeze node.
​compile.compile_brevitas_qat_model: Compile a Brevitas Quantization Aware Training model.
​compile.compile_onnx_model: Compile a torch module into a FHE equivalent.
​compile.compile_torch_model: Compile a torch module into a FHE equivalent.
​compile.convert_torch_tensor_or_numpy_array_to_numpy_array: Convert a torch tensor or a numpy array to a numpy array.
External Libraries
concrete.ml.common.check_inputs.md
Last modified 1mo ago